A brief study of image segmentation using Thresholding Technique on a Noisy Image

Abstract

Image segmentation is usually accustomed distinguish the foreground from the background of an image. The main target of this paper is an effort to review Image Segmentation using Thresholding Technique on a picture corrupted by Gaussian Noise as well as Salt and Pepper Noise which is enforced using MATLAB software and the results obtained are studied and thereby mentioned, highlighting the techniques performance. The algorithm is demonstrated through the segmentation of color images. The classification accuracy of the proposed method is evaluated and a comparative study versus existing techniques is presented. The experiments were conducted on an extensive set of color images. Satisfactory segmentation results have been obtained showing the effectiveness and superiority of the proposed method.

HCNet: Hierarchical Context Network for Semantic Segmentation

“…Therefore, we propose a multi-scale guided presegmentation module, which can flexibly partition features into class-based homogeneous regions according to the supervised guidance of ground truth. As shown in Figure 3, the input feature map F is fed into three parallel dilated convolutions with dilation rates (1,3,5). Each convolution output feature map has 64 channels Then, the feature maps of the three branches are aggregated through element-wise addition.…”

“…Traditional methods mostly adopt machine learning algorithms to perform image segmentation with various techniques, such as thresholding [5], region growing [6], edge detection [7], [8], clustering [9], [10], etc. Most successful works are based on hand-crafted features, such as HOG [11], SIFT [12], etc.…”

DSPCANet: Dual-Channel Scale-Aware Segmentation Network With Position and Channel Attentions for High-Resolution Aerial Images

“…Existing semantic segmentation methods can be divided into two categories: traditional methods and deep learningbased methods. Many traditional methods utilize the machine learning algorithms for image segmentation, such as threshold [2]- [4], edge detection [5], [6], and clustering [7], most of which are based on handcrafted features (i.e., histograms of oriented gradient (HOG) [8], scale-invariant feature transform (SIFT) [9]). Recently, deep learning is considered as a promising method to solve the problem of semantic segmentation [10]- [14].…”

A Brief Review of Image Segmentation Based on Thresholding Technique

“…A rest reason (among others) for using this variety of approach is mainly related with the GA ability to deal with large, complex search spaces in situations where only minimum knowledge is available about the objective function [10]. For instance, these led Bhanu et al to adopt a GA to determine the parameter set that optimise the output of an available segmentation algorithm under various situations of image acquisition and is namely Phoenix segmentation algorithm [1].…”

“…Al. [1] In this paper an effort to review Image Segmentation using Thresholding Technique on a picture corrupted by Gaussian Noise as well as Salt and Pepper Noise is conducted. It is enforced using MATLAB software and the results obtained are studied and thereby mentioned, highlighting the techniques performance.…”